David Krause (Marquette U) has posted “Addressing the Challenges of Auditing and Testing for AI Bias: A Comparative Analysis of Regulatory Frameworks” on SSRN. Here is the abstract:
The widespread adoption of artificial intelligence (AI) has ushered in a new era of technological innovation, offering transformative benefits in problem-solving and operational efficiency across diverse sectors. However, as AI systems increasingly influence high-stakes decisions, issues of bias and fairness have emerged as critical ethical concerns. This paper explores the multifaceted nature of AI bias-including algorithmic, data-driven, and societal biases-and its pervasive impacts on individuals and communities. Through a comparative analysis of regulatory frameworks for AI bias testing across jurisdictions, this study identifies shared challenges, best practices, and opportunities for improvement. The findings underscore the need for robust regulatory standards that uphold ethical principles in AI use and ensure credible assessments of fairness through third-party audits. The paper proposes targeted recommendations to enhance existing frameworks and suggests new strategies to strengthen the transparency, reliability, and effectiveness of AI bias testing, ultimately supporting a more ethical and accountable AI landscape.